CLC number: TN911.73
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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HAN Yan-fang, SHI Peng-fei. Mean shift texture surface detection based on WT and COM feature image selection[J]. Journal of Zhejiang University Science A, 2006, 7(6): 969-975.
@article{title="Mean shift texture surface detection based on WT and COM feature image selection",
author="HAN Yan-fang, SHI Peng-fei",
journal="Journal of Zhejiang University Science A",
volume="7",
number="6",
pages="969-975",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0969"
}
%0 Journal Article
%T Mean shift texture surface detection based on WT and COM feature image selection
%A HAN Yan-fang
%A SHI Peng-fei
%J Journal of Zhejiang University SCIENCE A
%V 7
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%P 969-975
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0969
TY - JOUR
T1 - Mean shift texture surface detection based on WT and COM feature image selection
A1 - HAN Yan-fang
A1 - SHI Peng-fei
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 6
SP - 969
EP - 975
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0969
Abstract: mean shift is a widely used clustering algorithm in image segmentation. However, the segmenting results are not so good as expected when dealing with the texture surface due to the influence of the textures. Therefore, an approach based on wavelet transform (WT), co-occurrence matrix (COM) and mean shift is proposed in this paper. First, WT and COM are employed to extract the optimal resolution approximation of the original image as feature image. Then, mean shift is successfully used to obtain better detection results. Finally, experiments are done to show this approach is effective.
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